Of course, the discrete parameters of the structure of the CNN can not be updated by the gradient descent strategy. This is the same as in the reinforcement learning neural Turing machines[1]} if we regard the discrete structure parameters as the actions in the neural Turing machines. So theoretically we can train the systems also with the reinforcement learning to find the optimal structure parameters, while the back-propagation in [1]} is replaced by the equilibrium propagation. Also this framework might not work due to the complexity in practice, just as the neural Turing machine can only achieve simple algorithmic tasks.
